References
Splintering Papers:
1. Splintering with distributions: A stochastic decoy scheme for private computation, Praneeth Vepakomma, Julia Balla, Ramesh Raskar, (2020) (PDF)
Split Learning Papers:
1. Distributed learning of deep neural network over multiple agents, Otkrist Gupta and Ramesh Raskar, In: Journal of Network and Computer Applications 116, (2018) (PDF)
2. DISCO: Dynamic and Invariant Sensitive Channel Obfuscation, Abhishek Singh, Ayush Chopra, Vivek Sharma, Ethan Z. Garza, Emily Zhang, Praneeth Vepakomma, Ramesh Raskar, Accepted to CVPR 2021. (2021) (PDF)
3. FedML: A Research Library and Benchmark for Federated Machine Learning, (Baidu Best Paper Award at NeurIPS-SpicyFL 2020) (PDF)
4. NoPeek: Information leakage reduction to share activations in distributed deep learning, Praneeth Vepakomma, Otkrist Gupta, Abhimanyu Dubey, Ramesh Raskar, (2020) (PDF)
5. Split learning for health: Distributed deep learning without sharing raw patient data, Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar, Accepted to ICLR 2019 Workshop on AI for social good. (2018) (PDF)
6. Detailed comparison of communication efficiency of split learning and federated learning, Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar, (2019) (PDF)
7. ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries, Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, and Ramesh Raskar (2019) (PDF)
8. Split Learning for collaborative deep learning in healthcare, Maarten G.Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar (2019)
Survey Papers:
1. Advances and open problems in federated learning (with, 58 authors from 25 institutions!) (2019) (PDF)
2. No Peek: A Survey of private distributed deep learning, Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey, (2018) (PDF)
3. A Review of Homomorphic Encryption Libraries for Secure Computation, Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar, Ranjan Ramachandra, Santanu Bhattacharya, (2018) (PDF)
AutoML Papers:
1. Accelerating neural architecture search using performance prediction, Bowen Baker, Otkrist Gupta, Ramesh Raskar, Nikhil Naik, In: conference paper at ICLR, (2018) (PDF)
2. Designing neural network architecture using reinforcement learning, Bowen Baker, Otkrist Gupta, Nikhil Naik & Ramesh Raskar, In: conference paper at ICLR, (2017) (PDF)
Differential Privacy Papers:
1. Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval, Praneeth Vepakomma, Julia Balla, Ramesh Raskar, (2021) (PDF)
2. DAMS: Meta-estimation of private sketch data structures for differentially private COVID-19 contact tracing, Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar, PPML-NeurIPS 2020, (2020) (PDF)